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Facial recognition a system problem gamblers can’t beat?

Problem gamblers beware. The OLG is set to unveil a new facial recognition program.

iView Systems of Oakville has made a biometrics system for face recognition that will be used and unveiled in Ontario by the OLG for casinos in May, to help ID self-excluded gamblers. (Rene Johnston / TORONTO STAR)

Problem gamblers beware. The Ontario Lottery and Gaming Corp. — which critics say hasn't done enough to keep you out — plans to be up to the challenge this spring.

OLG is set to unveil a new facial recognition program at all 27 of its gambling facilities in Ontario. It's being praised as a high roller in the privacy protection game.

“It's the most privacy-protected system using biometric encryption in the world,” said Ann Cavoukian, Ontario's privacy commissioner, who approved the new system.

Beginning May, each person who enters an Ontario casino will have their face digitally scanned by a camera; that image will be run through a database of more than 15,000 people with gambling problems who have voluntarily put themselves on a banned list.

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The system relies on bone structure and specific points on the face — like the distance between your eyes, nose, and mouth.

If the computer finds a match, security is notified. If not, your image is discarded, and casual gamblers may play away.

The new system will replace the current practice of having security staff personally detect gamblers who have returned to a casino after placing themselves on the self-excluded list.

The existing system catches about 1,000 self-excluded gamblers each year — a number that critics say is woefully low and unacceptable, given that gambling addiction is a compulsive illness.

According to the Centre for Addiction and Mental Health, there are more than 300,000 problem gamblers in Ontario. Critics say these gamblers brought in 30 per cent of the OLG's $1.9 billion revenues from casino gambling last year.

The privacy component of the new facial recognition system was designed by University of Toronto biometric engineers, led by Prof. Kostas Plataniotis.

“Nothing like this exists for facial recognition,” says Karl Martin, one of the developers.

The team created a biometric encryption algorithm that ensures there is no permanent link between a biometric template of a person's face and their private information.

“If the data is stolen or falls into the wrong hands, it's essentially useless ... a scrambled template,” Martin said.

The algorithm was added to a platform created by iView Systems, an Oakville-based company that specializes in security and surveillance.

It will cost the OLG between $3 million and $5 million to outfit its sites with the equipment, which include top surveillance cameras similar to those used in major airports.

During the OLG's pilot project, the system had a 91 per cent rate of accurate identifications, according to the Privacy Commissioner's office. The exceptional results came after lighting and camera locations were optimized.

But, said Cavoukian, there more than 50 million visits to OLG sites each year, and most are not problem gamblers.

“You don't want to do anything to restrict the privacy of patrons who are coming in minding their own business,” she said.

OLG’s new 4-step self-exclusion program

1. Enrolment process — Images are taken for facial recognition process, conversation between the self-excluder and security is documented, and a digital form is signed agreeing to the terms of self-exclusion.

2. Detection — Cameras are located at the entrance and exit of each casino. Faces are scanned in real time and encrypted into a unique algorithm.

3. Tracking and identification — The self-excluded database is searched for a match of that algorithm. If detected, the self-excluder’s information is distributed to security. Security personnel double-check to make sure the system has identified a self-excluded person, and that no one has been falsely identified.

4. Enforcement — If a self-excluded person is detected in the casino, they are asked to leave and the incident is recorded in the database.

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